An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.

www.nature.com/scientificreports/ is attributed to its ability to automatically learn functions from domain-specific images 13 .Furthermore, transfer learning models 14 saves knowledge from one problem and can apply that knowledge on another problem.But the conventional CNN models such as Resnet50, AlexNet, Inception and Xception, etc., cannot be run on low computing power devices such as tablets, embedded chips, mobile phones and hence cannot be given to real time applications 15,16 .These conventional models are also complex, need a lot of training time.To overcome these shortcomings, the lightweight 17 and concise models of CNNs are being developed which are having lesser number of parameters than the conventional CNNs so that they can be executed on devices with low computing power and smaller memory requirements.Figure 1 shows the features of concise CNNs.
In the case of CNNs, there is also a need to preprocess the image to get a better classification and for that purpose, image enhancement techniques 18 are used in which mask and filters upgrade the quality of image.In addition to that data augmentation techniques 19 increase the training data to upgrade the successful rate of model.The goal of this study is to present a new, simple CNN-based model for diagnosing COVID 19 from X-ray pictures, and the proposed model has been compared to existing transfer learning methods using a number of different criteria.The following are the primary contributions of this research work: (1) a novel set of layers, as well as image enhancement and hyperparameter tuning of parameters, have been suggested for the classification of COVID-19, normal, and pneumonia cases.(2) In order to prevent the models from overfitting, data augmentation has also been carried out.(3) The proposed framework may be used as one of the effective methods for classifying data in the medical industry.Furthermore, it helps radiologists diagnose and treat ailments earlier.
The paper is divided into the sections listed below.The second section discusses the literature review and the third section explains our suggested model.Materials and methods are discussed in fourth section.Fifth section includes a description of the experimental outcomes.Conclusions and future work are discussed in sixth section.

Literature survey
Recently, most frequently research has been going on in the domain of diagnosis disease using CNN from images.This section summarizes some of the existing works for disease diagnosis.The comparison of models performances are shown in Table 1.
Litjens et al. 29 proposed application aspects in deep learning.The different deep learning techniques extracted the spatial features from sophisticated image data i.e.CT, X-ray images, color Fundus images, ultrasound image  and implemented models, which can be helpful in hospitals to detect severe diseases such as diabetic retinopathy, skin lesion, bone fracture and breast cancer at their early stages.Kermany et al. 30 used optical coherence tomography images dataset to detect viral pneumonia and macular degeneration and diabetic retinopathy.Cao et al. 3 introduced deep learning, and the image analysis is done by deep learning architecture such as RNN, CNN and Stacked machine auto encoder.With these models, the detection of pediatric pneumonia with chest X-ray images can be done.The authors also presented the challenges in handling unlabeled data, privacy issues in the medical field, and many more.Jaiswal et al. 31 in their work used the region of interest, align convolution layer and pixel-wise segmentation of disease.Toğaçar et al. 14 proposed a minimum redundancy maximum relevance (mRMR) model for the diagnosis of pneumonia.The three knowledge transfer models, namely, VGG-16, Alex Net, and VGG-19,are used in the proposed architecture.Moreover, decision tree, linear Discriminant analysis, k-nearest neighbor, and support vector machines are used for grouping using features generated by transfer learning model.Singh et al. 32 proposed multi-objective differential evolution model for the classification of the COVID 19 disease.An exponential crossover algorithm is used.The proposed model gives high accuracy as compared to adaptive neuron fuzzy inference system, artificial neural network, and CNN types.
Das et al. 33 designed an Xception model to diagnosed COVID infection using chest X-ray dataset containing three classes pneumonia and COVID 19 negative, COVID 19 positive, and other infections except for COVID.The features are extracted by using different masks applied to the convolution layer.As a loss function, the cross-entropy is utilized.Brunese et al. 34 built two models: the first model assesses whether the picture belongs to a healthy patient or a patient suffering from general pulmonary illness.If the patient has a general pulmonary condition, the X-ray picture is sent to the second model, which checks whether it is a COVID 19 patient or pulmonary disease only.
Liu et al. 7 suggested a model for dental disease diagnosis utilizing a mask region-based convolution neural network with classification of seven different dental diseases.The model uses an IoT platform for patients to upload their dental images.A broad-level prototype is also given in the paper for dental image acquisition.Jain et al. 35 presented four phases of model where ResNet50 network is used to differentiate between bacterial pneumonia and pneumonia.Varela et al. 36 suggested approach uses feature extraction to minimize the number of pixels, grey level co-occurrence matrix features that focus on the spatial relationship between pixels, and the local binary patterns method to encode the pixel values.Marques et al. 37 has been suggested efficientnetb4 model is a convolution neural network.Ezzat et al. 38 suggested a technique to identify the optimal settings for hyperparameters, the gravitational search method is utilized as an optimization tool.The new method is contrasted with Social Ski Driver-Dennsenet121. Data preparation, hyperparameter selection, and the learning step for COVID 19 diagnosis are all part of the technique.Hassantabar et al. 39 has been proposed technique for detecting COVID 19 patients.Two approaches are utilized for diagnosis.The first is a deep neural network, while the second is an image segmentation approach for detecting diseased areas.Table 1 summarizes studies relating to COVID 19 and CNN architecture on chest x ray images (Type1) and PIMA and UCI (Type2), as well as information on additional approaches utilized in the papers.
Various studies have explored the application of deep learning techniques across diverse imaging modalities, including CT scans, X-rays, color fundus images, ultrasound, and optical coherence tomography [40][41][42] .The investigated diseases range from diabetic retinopathy and skin lesions to bone fractures, breast cancer, viral pneumonia, and COVID-19 13,43 .These studies employ a variety of deep learning architectures such as RNNs, CNNs, and stacked machine auto encoders.Notably, researchers have addressed challenges like handling unlabeled data and privacy issues in the medical field 44 .Key findings include the efficacy of models like Xception for diagnosing COVID-19 from chest X-ray images, the use of multi-objective models for COVID-19 classification, and innovative approaches like dental disease diagnosis using a mask region-based CNN 45 .The comparison in Table 1 underscores the performance of different models in COVID-19 diagnosis and CNN architecture across chest X-ray images and datasets like PIMA and UCI.Overall, these studies demonstrate the versatility and potential impact of deep learning in advancing early disease detection and diagnostic accuracy in medical imaging.

Proposed concise CNN based architecture
The framework of a convolutional neural network depends on the number of layers, activation function, optimizer, number of filters and batch size [46][47][48] .Figure 2 represents the proposed architecture of the COVID 19 diagnosis structure.The proposed model has been derived from the baseline Efficient Net model 46 .In contrast to the more complex architectures, the goal is to develop a concise CNN model that can identify picture modification 40 .The layers of the efficient Net model have are modified by replacing the MBconv layer with a Conv2D layer and also by updating the values of filter in layers additionally,the dropout layer is added with a 0.4 value to reduce overfitting of the model and add regularization.The proposed architecture is a sequential model.Additionally, the layers are added in the sequence order to build the CNN architecture.The proposed CNN model contains Conv2D, Maxpooling2D, Dropout, the Relu activation function, dense/fully connected layer.The suggested model has nine total layers: three convolutional, three maxpooling, three Relu, two dense, one dropout, and a fully connected layer.Convolutional layers are used in conjunction with the most common Rectified linear unit (ReLU) activation function to increase the performance and generalization by introducing non-linearities to the network.The vanishing gradient issue that may be seen in other forms of activation functions is eliminated by ReLU by correcting the values of inputs less than zero.ReLU's key benefit is quicker execution, which shortens computation time.The maxpool2D is used with each convolution layer to extract the best features.The description of each layer is represented in Table 2 in which Conv2D as T1 layer, Max_pooling2D as T2 layer, Dropout as T3 layer, Flatten as T4 layer and Fully connected as T5 layer.
The filter applied on the image is represented in Eq. ( 1).The h is a kernel and input image is represented as f.The resulting matrix of indexes of rows and columns is marked as q, r. ∑ a sign is used to add all values with limits j and k.
After this process, the filter is placed over the image and the value is multiplied by the value from the image.Then all values sum up and the feature map is generated.The padding is added to the image to fix the size in proper form.Equation ( 2) is used for padding.
The preprocessing part with CONV2D now moves to the pooling process.The formula for the pooling function is defined in Eq. ( 3).In the pooling process, we find the maximum and average according to the pool type.It is a technique to get sample feature maps from all features.It extracts features that contain high value during the sliding window extraction process.Here, s is the stride, n H is a size of height, nc is number of channels, n w is size of width.
For improved results, Relu is employed as an activation function in each CONV2D layer and maxpooling layer.The function work as Eq. ( 4), where x is the input value The convolution layer with maxpooling value is then direct to the feed forward function to calculate the value for the next step, Eq. ( 5) represents the functioning of this process Here, g is the activation function in Eq. ( 6), firstly the value of Z is calculated from the previous layer with W tensor and then bias b is added to it.
After calculating it, we move to the calculation of derivatives which will be used to update the value of parameter also known as gradient descent.The formula of a partial derivative as dW and db are parameters that work on the present layer.According to the chain rule, the result is Eq. ( 8) After the backpropagation process, hyperparameters tuning is the next step which includes checking parameter values with different patterns based on the performance of the model.The parameter used for tuning are: loss function, learning rate, optimizer and number of neurons.
( where y hat represent the outcome produced by the model and y represents the expected outcome. (b) Optimizer: The goal of an optimizer is to minimize losses by adjusting relevant model parameters like the learning rate and the weights.In the proposed approach, RMSprop is used as optimizer.The RMSprop takes the cumulative sum of the squared gradient represented in Eqs. ( 10) and ( 11) Here, αt learning rate at time t, δwt derivative of weight at time t and δl derivative of the loss function, v t sum of the square of past gradient, e small positive constant (10 -8 ) and β is moving average parameter (constant, 0.9).Dense Layer receives input from all neurons of the previous layer along with the Relu activation function.The dense layer return output is represented in Eq. (12)   In the above equation, o is output g is the activation function, dot represents numpy function for calculation, I is input.K represents the weight data, while b is the bias used to optimize the model.Figure 3 depicts the study's step-by-step process.The classification process of CNNs is to process input images through convolutional, activation, pooling, and fully connected layers.Training involves optimizing weights via backpropagation to minimize a loss function.The trained model predicts image classes by analyzing learned features.

Materials and methods
The description of the dataset 49 that was utilized in the experiment is configured into two types.The firstly used dataset in the experiment is dataset with data augmentation 44 .The second dataset used in the experiment is with image enhancement using hyper parameter tuning, data augmentation and Gaussian Blur.

Dataset description
The suggested model for detecting COVID 19 illness was evaluated using a dataset of publically accessible conventional chest X ray images 50 4 shows the sample image from each class of the test dataset.Total of 1000 images are taken from a dataset and then divided into different three samples.

Image enhancement
Gaussian blur feature is derived by blurring an image using Gaussian function.This technique upgrade the quality of an image and is helpful in finding inadequate information for image interpretation 51 .The spatial filtering, slicing, stretch, edge sharpening and other methods are used in this technique.The method reduces noise and increases smoothening of image.The process is achieved by convolving on image with Gaussian kernel.The formula used for the process is shown in Eq. ( 13) 18 : Here the distribution by standard deviation is denoted by σ and x, y are location indices.The Gaussian distribution mean value depends on the value of σ which influence the extent of blurring affect around pixel.The COVID and viral pneumonia images after and before Gaussian blur is shown in Figs. 5 and 6.
The opencv2 is used to implement Gaussian blur.The three functions are used as argument in the process i.e. img used to modified the image, sigma used in the x and y direction and truncate used to determining the limits of the approx.The Gaussian filter takes the x, y pixel and returns a single number by calculating the weighted average based on the normal distribution.
Figure 5 shows the images of chest X ray of covid class with and without image enhancement using Gaussian Blur 18 technique.Figure 6 shows the images of chest X-ray of Viral-pneumonia classes with and without image enhancement using Gaussian Blur technique.The paper results contain experimentation on two type of dataset i.e. with Gaussian Blur images dataset and without Gaussian Blur images dataset.1.The image pixel take the average value of its surrounding pixels.The center value is considered as average value.
2. Gaussian function is applied to get two dimensional normal distribution as 3. The value of this function will create convolution matrix/kernel.4. Gaussian blur is calculated based on the weight matrix.5.The function is applied on the entire image in the calculation for each image.6.In the end the Gaussian continuous value is converted into discrete values needed for a kernel.

Algorithm of Gaussian Blur
The CLAHE 52 and histogram equalization 53 techniques are experimented before the selection of Gaussian Blur.
The CLAHE define as contrast limiting adaptive histogram equalization which refine the images with high intensity.To improve contrast of image, histogram equalization is used.The other two techniques give less accuracy as compared to the blur technique 51 .

Data augmentation
In order to make training data more generalizable and applicable, data augmentation involves transforming images in various ways, such as rotating, flipping, and resizing 54 .Figure 7 shows the images without data augmentation and with data augmentation 19 .It increases the size of samples used as training set by applying different techniques written in the Table 2 which help the model to extract features and understand the image.The technique provide good results for enhancing the performance and is used to reduce over fitting 45 .The data augmentation methods is represent in Table 3 with different parameters.
The augmentation algorithms include kernel filters, geometric transformations, random erasing, mixing images, color space augmentations, etc.The above results show that preprocessing of image with data augmentation can increase the precision of classification and reduces the overfitting problem.

Evaluation parameters
Based on the confusion matrix, we will determine the class-wise performance of our model based on the following performance metrics 55 .

Experimental results and discussion
This part represents and analyze the results obtained after performing experiments in three different scenarios.All the proposed approaches have been executed with python using Tensor flow and Keras libraries 38 .For the analysis of results, dataset is categorized into different ratios as represented in Table 4.

Scenario 1
The following results are based on scenario 1, i.e., dataset ratio of 70:30 in which 70% of images belong to the training set and 30% belong to the testing dataset.After hyperparameter tuning step, the suggested approach is compared to other models.Figure 8 represents the confusion matrix.
Figure 8 represents the confusion-matrix of bifurcation of the dataset.Figure 9 represents the percentage of success for classifying COVID disease in 500 epochs.The proposed model gives 96% accuracy which is better   10 sum up the time of execution.The proposed approach takes less time as contrast to other models because it is lightweight and used fewer parameter which makes it faster than other models.Moving ahead, Table 5 shows the performance metrics obtained in scenario 1, containing the value of F1-score, recall and precision.In this table, the proposed model shows the highest value of F1-score, recall and precision for class COVID and viral-pneumonia.Figure 10 represents the testing loss of each model with 500 epochs.The loss value shows how much error rate is there in the model performance, the resulting graph shows that Resnet50 has high value of loss rate as compared to other models.Figure 11 illustrates the testing loss with 500 epochs for all models.

Scenario 2
The following results are based on scenario 2 with a dataset ratio of 60:40 in which 60% of images belong to the training dataset and 40% belong to testing dataset.Figure 12 shows a confusion matrix that illustrates that the proposed model and Inception model have high true positive value i.e. 141 and 142 as compared to other models.In the confusion matrix true positive value of proposed model is 141 which means the COVID images are correctly classified as COVID and 136 as true negative value which shows the viral pneumonia images correctly classified.
Table 6 displays the f1-score, recall and precision value of models.The model outperforms the Inception model in precision and surpasses the Xception model in recall.The projected model gives the maximum value of precision and recall which makes it better than other models.
Figure 13 represents the result of truly and correctly classified images of viral-pneumonia and COVID.The testing accuracy with Resnet50, Inception and XCeption is less as compared to the proposed model.Figure 14 displays the bar graph of the time executed by each model during the execution of the 500 epochs.
The proposed model was executed in 7460 s in total which is less than other transfer learning CNN models.The proposed model was executed in less time because the model architecture have lesser parameters as compared to other models.Figure 15 shows the result of validation loss rate with Resnet50, Inception and Xception.Loss defines how many the wrong predictions were made by the model.The proposed model gives less value of loss rate as compared to other models.7 displays the significance of the findings in terms of accuracy, recall, and f1-score of Inceptionv3, Resnet50, Xception, and the proposed model while using Scenario 3. Figure 17 represent the increasing success rate of classified data of proposed model as compared to     Figure 20 shows the confusion matrix of image enhancement dataset in scenario 1.The confusion matrix shows the truly identified images by model and help in deep analysis of model performance.Figure 21 represents the true positive and true negative correctly identified classes of dataset in 500 epochs.The proposed model gives 98% accuracy which is better than other model's accuracy.The bar graph shows in Fig. 22 show the execution time of the model.The proposed model architecture contains less parameter which decrease the execution time.8 shows performance metrics obtained in scenario 1. Accuracy can be misleading if used with imbalanced dataset and therefore the other metrics such as f1-score, recall and precision useful are for evaluation.Table 6 is the summarization of the results of each model.Figure 23 represents the loss rate of each model with 500 epochs.The Loss value shows how much error rate is there in the model's performance, the resulting graph shows that Resnet50 has high value of loss rate as compared to other models.

Scenario 2
The following results are based on scenario 2 with a dataset ratio of 60:40 in which 60% of images belong to the training dataset and 40% belong to testing dataset.The classifier matrix has been portrayed in Fig. 24, which shows the true positive value of proposed model is 151 which is high than others.Table 9 give the performance     www.nature.com/scientificreports/approach is 0.98.When compared to other models, the Xception produces lower value results.Figure 30 shows the execution time of each model in 500 epochs.Figure 31 exhibits the validation loss outcome along with the result analysis, providing the count of actual and predicted labels for all classes.
Precision values show that out of all the non-COVID 19 infected cases, how many were identified correctly by the model.Figure 32 shows the overall precision value of Resnet50, Inception, Xception and the proposed approach.Figure 32a

Aggregation of three scenarios
This section presents the average of all the results obtained in the above mentioned three scenarios.The results are the average of the model which includes the three phases: image enhancement, data augmentation and hyperparameter tuning.The average value of recall, precision, f1-measure and accuracy based on the average of all results with image enhancement dataset.
Recall value shows out of all the COVID 19 patients, how many were identified correctly by the model.Figure 33 shows the overall recall value (in percentage) of Resnet50, Inception, Xception and the proposed approach.Figure 33a  Unfortunately, it is not feasible to maximize both accuracy and recall simultaneously.So, there is another metric available for convenience called F-measure.Figure 34a shows f1-score values for class COVID 19 as Resnet50 is 87.3%, Inception gives a value of 91%, XCeption gives a value of 90% and proposed model is 96%. Figure 34b shows the values for class Viral pneumonia as Resnet50 is 86.3%, Inception gives a value of 93%, XCeption gives a value of 92% and for the proposed model it is 97%.
Figure 35 shows the average accuracy of all models based on the different experiments.The average accuracy of Resnet50 is 0.89, Inception accuracy is 0.93, the accuracy of XCeption is 0.92 and proposed model accuracy is 0.98.The proposed model perform better in comparison to other models as shown in Figure 36 as ROC curve of models.
Table 11 represent the evaluation metrics such as sensitivity, specificity, positive and negative likelihood to enhance the comprehensibility of the model's performance.The proposed model give better results than other transfer learning models.
It can be clearly gauged from the above result that the model proposed here is better than various other models in terms of various parameters.The outperformance of draft model can be credited to the various modifications to the CNN architecture, which can be further summarized by the following points.

Conclusion
In current times, deep learning models have come into existence and are playing a massive role in the development of various computer aided disease diagnosis systems.In this research, an attempt has been made to develop a concise CNN model by using lesser number of parameters (no. of layers, kernel size, optimizer, activation function) so as to reduce the execution time while obtaining a better classification accuracy for the diagnosis of COVID-19.The experiments have been performed on various X-ray images, using Gaussian Blur as an image enhancement technique and image augmentation in the image preprocessing stage.The performance of the proposed model is also compared with existing transfer learning models viz.Xception, Resnet50 and inception.The results clearly indicate the outperformance of the proposed model in terms of various parameters (accuracy, loss rate, precision, recall and f1-score).In near future, the proposed model can also be implemented for diagnosis of various other diseases and other different datasets of COVID-19.To reduce the dimensionality of data the various nature inspired meta-heuristic algorithms can be deployed to select the most dominant features while removing the redundant or less important features from the images so as to reduce the computation time and increasing the accuracy of classification.
a.The size of image as input is 224 × 224 × 3, i.e., 224 is height and width of image and 3 is image channel value as RGB.The first convolution layer (L1) represent the first layer of model takes an input of size 224 × 224 × 3 and has kernel size 3 × 3 which produces 32 features maps as result.b.The second convolution layer (L2) has 32 filters and has kernel size 3 × 3 which produces 32 features maps as result.c.The third convolution layer (L3) contain 64 filters with kernel size 3 × 3 which produces tensor of 64 features maps as result.https://doi.org/10.1038/s41598-024-51317-yd.To tackle the overfitting problem, the above layers are followed by dropout layer with 64 filters having kernel size of 3 × 3. Dropout layer is followed by flattening layer.The flatten layer converts the data into 1-D form.e.In last the dense/fully connected layer is added with 128 filters and the efficiency of the model is improved by Relu as activation function which produces 258 features.This layer produces the output.

Figure 2 .
Figure 2. Architecture of proposed model (source of clipart images: draw.io).

Figure 3 .
Figure 3. Phases undertaken in the proposed framework.

Figure 6 .
Figure 6.Image enhancement result on viral pneumonia dataset.

Figure 10 .
Figure 10.Execution time of each model.
www.nature.com/scientificreports/Scenario3The following results are based on scenario 3 on the dataset ratio 80:20 in which 80% of images belong to the training dataset and 20% belong to testing dataset.The confusion matrix which shows the result of model performance in predicting true images of diseases has been illustrated in Fig.16

Figure 14 .
Figure 14.Execution time by each model.

Figure 22 .
Figure 22.Execution time by each model.
represents the values for class COVID 19 as Resnet50 is 95.6, Inception gives a value of 90, Xception gives a value of 88.6 and proposed model is 95.3.Figure 33b shows the values for class Viral pneumonia as Resnet50 is 87, Inception gives a value of 95, Xception gives a value of 93 and proposed model is 98.

2 .
Authors have also used the concept of data augmentation.It used to large the quantity data by adding slightly modify samples of data images.It can reduce the overfitting of model and also increase classification value by increasing the training data.The below figure clearly depicts the effect of image augmentation.The following two figures show the successful rate and loss rate of draft model before and after data augmentation.The accuracy of the model by using 500 epochs is shown in Fig. 38.And loss of value on 500 epochs is shown in Fig. 39.

Figure 37 .
Figure 37. Image enhancement techniques based images.

Table 1 .
State of the art techniques.

Table 2 .
Layer architecture of proposed model.It is used to compute the model error.The gradients may be calculated from the loss function and used to update the weights.To generate output, the suggested model uses a sparse categorical cross entropy loss function and the mathematical operation of which is shown in Eq.(9) Vol:.(1234567890) Scientific Reports | (2024) 14:1136 | https://doi.org/10.1038/s41598-024-51317-ywww.nature.com/scientificreports/(a) Loss function: . The collection includes 3616 COVID 19 positive cases, 10,192 Normal pictures, 6012 Lung Opacity, and 1345 viral Pneumonia images.Only two of the four types of images presented were taken into account in our experiments, i.e., COVID 19 positive and viral Pneumonia.Every image is a grayscale image consisting of 299 × 299 pixels.Figure

Table 5 .
Model results with scenario 1.

Table 6 .
Model results on scenario 2.

Table 8 .
Model results with scenario 1.
of models which shows the proposed approach model gives the better value of precision and recall which makes it better than other models.

Table 12 ,
represent the comparative analysis of all three image enhancement techniques with parameters accuracy and loss rate.

Table 11 .
Comparison table of models.

Table 14 .
Comparative analysis of our proposed model with state of the art approaches.